Reading Shakespeare Sonnets: Combining Quantitative Narrative Analysis and Predictive Modeling -an Eye Tracking Study

As a part of a larger interdisciplinary project on Shakespeare sonnets' reception (1, 2), the present study analyzed the eye movement behavior of participants reading three of the 154 sonnets as a function of seven lexical features extracted via Quantitative Narrative Analysis (QNA). Using a ma...

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Bibliographic Details
Published in:Journal of eye movement research 2019-03, Vol.12 (5)
Main Authors: Xue, Shuwei, Lüdtke, Jana, Sylvester, Teresa, Jacobs, Arthur M
Format: Article
Language:English
Subjects:
eye movements
eye tracking
Literary reading
predictive modeling
QNA
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Summary:As a part of a larger interdisciplinary project on Shakespeare sonnets' reception (1, 2), the present study analyzed the eye movement behavior of participants reading three of the 154 sonnets as a function of seven lexical features extracted via Quantitative Narrative Analysis (QNA). Using a machine learning-based predictive modeling approach five 'surface' features (word length, orthographic neighborhood density, word frequency, orthographic dissimilarity and sonority score) were detected as important predictors of total reading time and fixation probability in poetry reading. The fact that one phonological feature, i.e., sonority score, also played a role is in line with current theorizing on poetry reading. Our approach opens new ways for future eye movement research on reading poetic texts and other complex literary materials(3).
ISSN:1995-8692
1995-8692
DOI:10.16910/jemr.12.5.2